Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations467
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.0 KiB
Average record size in memory160.0 B

Variable types

Numeric14
Categorical4
Text1

Alerts

DEM is highly overall correlated with ID and 7 other fieldsHigh correlation
FIPS is highly overall correlated with STATE and 1 other fieldsHigh correlation
FRG is highly overall correlated with NLCDHigh correlation
ID is highly overall correlated with DEM and 6 other fieldsHigh correlation
KFactor is highly overall correlated with DEM and 1 other fieldsHigh correlation
Latitude is highly overall correlated with MAT and 2 other fieldsHigh correlation
Longitude is highly overall correlated with DEM and 6 other fieldsHigh correlation
MAP is highly overall correlated with NDVI and 1 other fieldsHigh correlation
MAT is highly overall correlated with DEM and 6 other fieldsHigh correlation
NDVI is highly overall correlated with MAP and 1 other fieldsHigh correlation
NLCD is highly overall correlated with DEM and 3 other fieldsHigh correlation
SOC is highly overall correlated with MAP and 1 other fieldsHigh correlation
STATE is highly overall correlated with DEM and 6 other fieldsHigh correlation
STATE_ID is highly overall correlated with DEM and 6 other fieldsHigh correlation
SiltClay is highly overall correlated with KFactorHigh correlation
Slope is highly overall correlated with DEM and 3 other fieldsHigh correlation
ID is uniformly distributed Uniform
ID has unique values Unique
Longitude has unique values Unique
Latitude has unique values Unique

Reproduction

Analysis started2025-09-15 01:16:15.746493
Analysis finished2025-09-15 01:16:32.574437
Duration16.83 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct467
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.88009
Minimum1
Maximum473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:32.649442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24.3
Q1120.5
median238
Q3356.5
95-th percentile449.7
Maximum473
Range472
Interquartile range (IQR)236

Descriptive statistics

Standard deviation136.80106
Coefficient of variation (CV)0.57508413
Kurtosis-1.1964052
Mean237.88009
Median Absolute Deviation (MAD)118
Skewness-0.0092600624
Sum111090
Variance18714.531
MonotonicityStrictly increasing
2025-09-14T21:16:32.758545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
473 1
 
0.2%
1 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
Other values (457) 457
97.9%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
473 1
0.2%
472 1
0.2%
471 1
0.2%
470 1
0.2%
469 1
0.2%
468 1
0.2%
467 1
0.2%
466 1
0.2%
465 1
0.2%
464 1
0.2%

FIPS
Real number (ℝ)

High correlation 

Distinct172
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29150.857
Minimum8001
Maximum56045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:32.863841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8001
5-th percentile8033
Q18109
median20193
Q356001
95-th percentile56035
Maximum56045
Range48044
Interquartile range (IQR)47892

Descriptive statistics

Standard deviation18391.875
Coefficient of variation (CV)0.63092059
Kurtosis-1.3278607
Mean29150.857
Median Absolute Deviation (MAD)12184
Skewness0.3351868
Sum13613450
Variance3.3826108 × 108
MonotonicityNot monotonic
2025-09-14T21:16:32.972298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56013 13
 
2.8%
56007 11
 
2.4%
56035 9
 
1.9%
56029 9
 
1.9%
35003 8
 
1.7%
56009 8
 
1.7%
35053 7
 
1.5%
56039 7
 
1.5%
56005 7
 
1.5%
56019 7
 
1.5%
Other values (162) 381
81.6%
ValueCountFrequency (%)
8001 1
 
0.2%
8003 2
 
0.4%
8005 3
0.6%
8007 1
 
0.2%
8009 5
1.1%
8013 1
 
0.2%
8015 1
 
0.2%
8017 3
0.6%
8021 1
 
0.2%
8023 1
 
0.2%
ValueCountFrequency (%)
56045 4
0.9%
56043 5
1.1%
56041 2
 
0.4%
56039 7
1.5%
56037 4
0.9%
56035 9
1.9%
56033 4
0.9%
56031 2
 
0.4%
56029 9
1.9%
56027 3
 
0.6%

STATE_ID
Categorical

High correlation 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
8
135 
56
118 
35
108 
20
106 

Length

Max length2
Median length2
Mean length1.7109208
Min length1

Characters and Unicode

Total characters799
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row56
2nd row56
3rd row56
4th row56
5th row56

Common Values

ValueCountFrequency (%)
8 135
28.9%
56 118
25.3%
35 108
23.1%
20 106
22.7%

Length

2025-09-14T21:16:33.066994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-14T21:16:33.136056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8 135
28.9%
56 118
25.3%
35 108
23.1%
20 106
22.7%

Most occurring characters

ValueCountFrequency (%)
5 226
28.3%
8 135
16.9%
6 118
14.8%
3 108
13.5%
2 106
13.3%
0 106
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 226
28.3%
8 135
16.9%
6 118
14.8%
3 108
13.5%
2 106
13.3%
0 106
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 226
28.3%
8 135
16.9%
6 118
14.8%
3 108
13.5%
2 106
13.3%
0 106
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 226
28.3%
8 135
16.9%
6 118
14.8%
3 108
13.5%
2 106
13.3%
0 106
13.3%

STATE
Categorical

High correlation 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
Colorado
135 
Wyoming
118 
New Mexico
108 
Kansas
106 

Length

Max length10
Median length8
Mean length7.7558887
Min length6

Characters and Unicode

Total characters3622
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWyoming
2nd rowWyoming
3rd rowWyoming
4th rowWyoming
5th rowWyoming

Common Values

ValueCountFrequency (%)
Colorado 135
28.9%
Wyoming 118
25.3%
New Mexico 108
23.1%
Kansas 106
22.7%

Length

2025-09-14T21:16:33.223459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-14T21:16:33.291942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
colorado 135
23.5%
wyoming 118
20.5%
new 108
18.8%
mexico 108
18.8%
kansas 106
18.4%

Most occurring characters

ValueCountFrequency (%)
o 631
17.4%
a 347
 
9.6%
i 226
 
6.2%
n 224
 
6.2%
e 216
 
6.0%
s 212
 
5.9%
r 135
 
3.7%
C 135
 
3.7%
l 135
 
3.7%
d 135
 
3.7%
Other values (11) 1226
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 631
17.4%
a 347
 
9.6%
i 226
 
6.2%
n 224
 
6.2%
e 216
 
6.0%
s 212
 
5.9%
r 135
 
3.7%
C 135
 
3.7%
l 135
 
3.7%
d 135
 
3.7%
Other values (11) 1226
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 631
17.4%
a 347
 
9.6%
i 226
 
6.2%
n 224
 
6.2%
e 216
 
6.0%
s 212
 
5.9%
r 135
 
3.7%
C 135
 
3.7%
l 135
 
3.7%
d 135
 
3.7%
Other values (11) 1226
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 631
17.4%
a 347
 
9.6%
i 226
 
6.2%
n 224
 
6.2%
e 216
 
6.0%
s 212
 
5.9%
r 135
 
3.7%
C 135
 
3.7%
l 135
 
3.7%
d 135
 
3.7%
Other values (11) 1226
33.8%

COUNTY
Text

Distinct161
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:33.515391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length17
Mean length13.646681
Min length10

Characters and Unicode

Total characters6373
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)11.3%

Sample

1st rowUinta County
2nd rowLincoln County
3rd rowTeton County
4th rowTeton County
5th rowPark County
ValueCountFrequency (%)
county 467
47.9%
fremont 13
 
1.3%
park 12
 
1.2%
rio 12
 
1.2%
carbon 11
 
1.1%
sublette 9
 
0.9%
converse 8
 
0.8%
san 8
 
0.8%
baca 8
 
0.8%
catron 8
 
0.8%
Other values (160) 418
42.9%
2025-09-14T21:16:33.842863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 760
11.9%
n 724
11.4%
t 615
9.7%
u 545
8.6%
C 539
8.5%
507
 
8.0%
y 504
 
7.9%
a 354
 
5.6%
e 288
 
4.5%
r 224
 
3.5%
Other values (39) 1313
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 760
11.9%
n 724
11.4%
t 615
9.7%
u 545
8.6%
C 539
8.5%
507
 
8.0%
y 504
 
7.9%
a 354
 
5.6%
e 288
 
4.5%
r 224
 
3.5%
Other values (39) 1313
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 760
11.9%
n 724
11.4%
t 615
9.7%
u 545
8.6%
C 539
8.5%
507
 
8.0%
y 504
 
7.9%
a 354
 
5.6%
e 288
 
4.5%
r 224
 
3.5%
Other values (39) 1313
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 760
11.9%
n 724
11.4%
t 615
9.7%
u 545
8.6%
C 539
8.5%
507
 
8.0%
y 504
 
7.9%
a 354
 
5.6%
e 288
 
4.5%
r 224
 
3.5%
Other values (39) 1313
20.6%

Longitude
Real number (ℝ)

High correlation  Unique 

Distinct467
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-104.47734
Minimum-111.01186
Maximum-94.915421
Zeros0
Zeros (%)0.0%
Negative467
Negative (%)100.0%
Memory size7.3 KiB
2025-09-14T21:16:33.946504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-111.01186
5-th percentile-109.61022
Q1-107.53871
median-105.30963
Q3-102.59252
95-th percentile-96.20034
Maximum-94.915421
Range16.096439
Interquartile range (IQR)4.946195

Descriptive statistics

Standard deviation3.9984567
Coefficient of variation (CV)-0.038271041
Kurtosis-0.30793242
Mean-104.47734
Median Absolute Deviation (MAD)2.3804851
Skewness0.7466149
Sum-48790.919
Variance15.987656
MonotonicityStrictly increasing
2025-09-14T21:16:34.057360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-94.91542121 1
 
0.2%
-111.01186 1
 
0.2%
-110.982973 1
 
0.2%
-110.80649 1
 
0.2%
-110.7344173 1
 
0.2%
-110.7307901 1
 
0.2%
-110.66185 1
 
0.2%
-110.64348 1
 
0.2%
-110.595819 1
 
0.2%
-110.5769801 1
 
0.2%
Other values (457) 457
97.9%
ValueCountFrequency (%)
-111.01186 1
0.2%
-110.982973 1
0.2%
-110.80649 1
0.2%
-110.7344173 1
0.2%
-110.7307901 1
0.2%
-110.66185 1
0.2%
-110.64348 1
0.2%
-110.595819 1
0.2%
-110.5769801 1
0.2%
-110.51702 1
0.2%
ValueCountFrequency (%)
-94.91542121 1
0.2%
-95.01079355 1
0.2%
-95.18900004 1
0.2%
-95.23890906 1
0.2%
-95.3651564 1
0.2%
-95.36933051 1
0.2%
-95.37215319 1
0.2%
-95.47712358 1
0.2%
-95.48475339 1
0.2%
-95.52098663 1
0.2%

Latitude
Real number (ℝ)

High correlation  Unique 

Distinct467
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.861363
Minimum31.50637
Maximum44.989732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:34.171113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31.50637
5-th percentile32.945652
Q137.188095
median38.753447
Q341.039265
95-th percentile44.157793
Maximum44.989732
Range13.483362
Interquartile range (IQR)3.85117

Descriptive statistics

Standard deviation3.2249022
Coefficient of variation (CV)0.082984793
Kurtosis-0.52820151
Mean38.861363
Median Absolute Deviation (MAD)1.8699929
Skewness-0.10346937
Sum18148.257
Variance10.399994
MonotonicityNot monotonic
2025-09-14T21:16:34.279086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.28449297 1
 
0.2%
41.05630002 1
 
0.2%
42.88349671 1
 
0.2%
44.53497 1
 
0.2%
44.43288644 1
 
0.2%
44.80635003 1
 
0.2%
44.09124 1
 
0.2%
43.51083 1
 
0.2%
44.31814975 1
 
0.2%
43.69342003 1
 
0.2%
Other values (457) 457
97.9%
ValueCountFrequency (%)
31.50637004 1
0.2%
31.80685194 1
0.2%
31.89556046 1
0.2%
31.93259307 1
0.2%
32.17824002 1
0.2%
32.19851001 1
0.2%
32.23849002 1
0.2%
32.27705996 1
0.2%
32.31215996 1
0.2%
32.35368689 1
0.2%
ValueCountFrequency (%)
44.98973175 1
0.2%
44.95772118 1
0.2%
44.92136235 1
0.2%
44.91846863 1
0.2%
44.84582998 1
0.2%
44.83989999 1
0.2%
44.80635003 1
0.2%
44.77946004 1
0.2%
44.7719228 1
0.2%
44.75683001 1
0.2%

SOC
Real number (ℝ)

High correlation 

Distinct456
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3507623
Minimum0.408
Maximum30.473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:34.384571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.408
5-th percentile0.9637
Q12.7695
median4.971
Q38.7135
95-th percentile16.5219
Maximum30.473
Range30.065
Interquartile range (IQR)5.944

Descriptive statistics

Standard deviation5.0454091
Coefficient of variation (CV)0.79445725
Kurtosis2.4271923
Mean6.3507623
Median Absolute Deviation (MAD)2.619
Skewness1.4647284
Sum2965.806
Variance25.456153
MonotonicityNot monotonic
2025-09-14T21:16:34.492624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.02 2
 
0.4%
2.884 2
 
0.4%
1.015 2
 
0.4%
3.595 2
 
0.4%
11.22 2
 
0.4%
4.594 2
 
0.4%
1.35 2
 
0.4%
10.076 2
 
0.4%
4.83 2
 
0.4%
4.974 2
 
0.4%
Other values (446) 447
95.7%
ValueCountFrequency (%)
0.408 1
0.2%
0.446 1
0.2%
0.462 1
0.2%
0.471 1
0.2%
0.485 1
0.2%
0.494 1
0.2%
0.5 1
0.2%
0.528 1
0.2%
0.605 1
0.2%
0.606 1
0.2%
ValueCountFrequency (%)
30.473 1
0.2%
27.984 1
0.2%
24.954 1
0.2%
23.19 1
0.2%
23.058 1
0.2%
21.644 1
0.2%
21.591 1
0.2%
21.455 1
0.2%
21.125 1
0.2%
21.076 1
0.2%

DEM
Real number (ℝ)

High correlation 

Distinct461
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1632.0277
Minimum258.6488
Maximum3618.0242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:34.596487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum258.6488
5-th percentile353.0946
Q11175.3314
median1592.8932
Q32238.2736
95-th percentile2803.103
Maximum3618.0242
Range3359.3754
Interquartile range (IQR)1062.9422

Descriptive statistics

Standard deviation770.28769
Coefficient of variation (CV)0.47198199
Kurtosis-0.81530352
Mean1632.0277
Median Absolute Deviation (MAD)590.4635
Skewness-0.026257609
Sum762156.94
Variance593343.12
MonotonicityNot monotonic
2025-09-14T21:16:34.703357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1561.74939 2
 
0.4%
2465.521973 2
 
0.4%
2439.060547 2
 
0.4%
313.8303223 2
 
0.4%
1986.571533 2
 
0.4%
2328.449463 2
 
0.4%
1331.444092 1
 
0.2%
1063.505127 1
 
0.2%
1313.987915 1
 
0.2%
1125.764282 1
 
0.2%
Other values (451) 451
96.6%
ValueCountFrequency (%)
258.6488037 1
0.2%
269.3353577 1
0.2%
272.2623901 1
0.2%
272.6796875 1
0.2%
277.6633606 1
0.2%
293.8309326 1
0.2%
296.3933716 1
0.2%
298.0536804 1
0.2%
307.7427063 1
0.2%
313.1699219 1
0.2%
ValueCountFrequency (%)
3618.02417 1
0.2%
3471.049316 1
0.2%
3376.358154 1
0.2%
3376.141846 1
0.2%
3232.158203 1
0.2%
3124.866211 1
0.2%
3123.391602 1
0.2%
3111.41333 1
0.2%
3109.554443 1
0.2%
3095.490479 1
0.2%

Aspect
Real number (ℝ)

Distinct461
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.33736
Minimum86.894569
Maximum255.83353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:34.810862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum86.894569
5-th percentile129.9651
Q1148.79188
median164.04301
Q3178.81433
95-th percentile208.20043
Maximum255.83353
Range168.93896
Interquartile range (IQR)30.022453

Descriptive statistics

Standard deviation24.383587
Coefficient of variation (CV)0.14747779
Kurtosis0.98005857
Mean165.33736
Median Absolute Deviation (MAD)15.235626
Skewness0.54757918
Sum77212.546
Variance594.55934
MonotonicityNot monotonic
2025-09-14T21:16:34.918811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.082428 2
 
0.4%
136.4089966 2
 
0.4%
153.8435516 2
 
0.4%
176.8919983 2
 
0.4%
175.594574 2
 
0.4%
219.6880188 2
 
0.4%
170.2262115 1
 
0.2%
164.7299347 1
 
0.2%
197.0122223 1
 
0.2%
172.140686 1
 
0.2%
Other values (451) 451
96.6%
ValueCountFrequency (%)
86.8945694 1
0.2%
106.511261 1
0.2%
110.139595 1
0.2%
110.2962875 1
0.2%
115.2586517 1
0.2%
117.2919388 1
0.2%
118.9809952 1
0.2%
120.0071259 1
0.2%
120.4855576 1
0.2%
121.1834183 1
0.2%
ValueCountFrequency (%)
255.8335266 1
0.2%
254.6745758 1
0.2%
250.3361969 1
0.2%
250.1024017 1
0.2%
234.4831238 1
0.2%
232.394577 1
0.2%
229.5141144 1
0.2%
224.0729828 1
0.2%
223.6387939 1
0.2%
223.1869812 1
0.2%

Slope
Real number (ℝ)

High correlation 

Distinct461
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8398913
Minimum0.64925271
Maximum26.104162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:35.027503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.64925271
5-th percentile0.93178515
Q11.4506671
median2.7266774
Q37.1352777
95-th percentile14.746026
Maximum26.104162
Range25.45491
Interquartile range (IQR)5.6846106

Descriptive statistics

Standard deviation4.7031429
Coefficient of variation (CV)0.97174558
Kurtosis2.4021878
Mean4.8398913
Median Absolute Deviation (MAD)1.5953804
Skewness1.6249347
Sum2260.2292
Variance22.119553
MonotonicityNot monotonic
2025-09-14T21:16:35.132285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.469407558 2
 
0.4%
10.10852718 2
 
0.4%
3.570212603 2
 
0.4%
2.361840725 2
 
0.4%
4.573308945 2
 
0.4%
10.4627409 2
 
0.4%
1.872875452 1
 
0.2%
1.025621295 1
 
0.2%
1.252177596 1
 
0.2%
1.311453462 1
 
0.2%
Other values (451) 451
96.6%
ValueCountFrequency (%)
0.649252713 1
0.2%
0.753047287 1
0.2%
0.781253636 1
0.2%
0.785101414 1
0.2%
0.789641201 1
0.2%
0.800130606 1
0.2%
0.805040777 1
0.2%
0.817521036 1
0.2%
0.835930109 1
0.2%
0.858109653 1
0.2%
ValueCountFrequency (%)
26.10416222 1
0.2%
24.94082069 1
0.2%
20.80967331 1
0.2%
20.4222908 1
0.2%
20.31998825 1
0.2%
19.7778759 1
0.2%
19.34244156 1
0.2%
19.16675568 1
0.2%
18.98168182 1
0.2%
18.61675262 1
0.2%

TPI
Real number (ℝ)

Distinct461
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0093653942
Minimum-26.708651
Maximum16.706257
Zeros0
Zeros (%)0.0%
Negative238
Negative (%)51.0%
Memory size7.3 KiB
2025-09-14T21:16:35.740876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-26.708651
5-th percentile-5.2779879
Q1-0.80491722
median-0.042385288
Q30.86288306
95-th percentile6.272398
Maximum16.706257
Range43.414907
Interquartile range (IQR)1.6678003

Descriptive statistics

Standard deviation3.5782994
Coefficient of variation (CV)382.07675
Kurtosis10.679155
Mean0.0093653942
Median Absolute Deviation (MAD)0.84350365
Skewness-1.0926743
Sum4.3736391
Variance12.804226
MonotonicityNot monotonic
2025-09-14T21:16:35.843596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.700242758 2
 
0.4%
8.48152256 2
 
0.4%
-2.728082418 2
 
0.4%
-0.885888934 2
 
0.4%
1.805906773 2
 
0.4%
0.551476836 2
 
0.4%
-0.133461028 1
 
0.2%
0.251985759 1
 
0.2%
1.17921257 1
 
0.2%
0.977267027 1
 
0.2%
Other values (451) 451
96.6%
ValueCountFrequency (%)
-26.70865059 1
0.2%
-16.92754936 1
0.2%
-14.92387867 1
0.2%
-13.91132736 1
0.2%
-12.696311 1
0.2%
-11.65337181 1
0.2%
-11.63264465 1
0.2%
-10.73035431 1
0.2%
-9.535725594 1
0.2%
-9.479537964 1
0.2%
ValueCountFrequency (%)
16.70625687 1
0.2%
11.70857334 1
0.2%
10.79698753 1
0.2%
10.26024818 1
0.2%
10.1651659 1
0.2%
9.453812599 1
0.2%
9.318076134 1
0.2%
9.142570496 1
0.2%
9.017038345 1
0.2%
8.48152256 2
0.4%

KFactor
Real number (ℝ)

High correlation 

Distinct383
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25583092
Minimum0.050000001
Maximum0.43000001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:35.947237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.050000001
5-th percentile0.10320121
Q10.19333572
median0.28
Q30.31999999
95-th percentile0.37000001
Maximum0.43000001
Range0.38000001
Interquartile range (IQR)0.12666427

Descriptive statistics

Standard deviation0.085746309
Coefficient of variation (CV)0.33516789
Kurtosis-0.64690782
Mean0.25583092
Median Absolute Deviation (MAD)0.054399997
Skewness-0.53879785
Sum119.47304
Variance0.0073524295
MonotonicityNot monotonic
2025-09-14T21:16:36.059011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.319999993 53
 
11.3%
0.370000005 10
 
2.1%
0.200000003 7
 
1.5%
0.280000001 6
 
1.3%
0.170000002 4
 
0.9%
0.359285712 2
 
0.4%
0.260937512 2
 
0.4%
0.106701031 2
 
0.4%
0.12565656 2
 
0.4%
0.116666667 2
 
0.4%
Other values (373) 377
80.7%
ValueCountFrequency (%)
0.050000001 2
0.4%
0.05102041 1
0.2%
0.053296704 1
0.2%
0.05468085 1
0.2%
0.055 1
0.2%
0.058526315 1
0.2%
0.059 1
0.2%
0.059500001 1
0.2%
0.05979592 1
0.2%
0.064130433 1
0.2%
ValueCountFrequency (%)
0.430000007 1
0.2%
0.412197798 1
0.2%
0.411818177 1
0.2%
0.410430104 1
0.2%
0.403950632 1
0.2%
0.392098755 1
0.2%
0.388297886 1
0.2%
0.38595745 1
0.2%
0.384375006 1
0.2%
0.377499998 1
0.2%

MAP
Real number (ℝ)

High correlation 

Distinct461
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.85489
Minimum193.91322
Maximum1128.1145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:36.170718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum193.91322
5-th percentile261.76863
Q1354.18263
median433.79749
Q3590.73346
95-th percentile927.77014
Maximum1128.1145
Range934.20128
Interquartile range (IQR)236.55083

Descriptive statistics

Standard deviation207.08874
Coefficient of variation (CV)0.41347054
Kurtosis0.44960916
Mean500.85489
Median Absolute Deviation (MAD)102.84637
Skewness1.0755008
Sum233899.23
Variance42885.747
MonotonicityNot monotonic
2025-09-14T21:16:36.275597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
369.6527405 2
 
0.4%
489.2418823 2
 
0.4%
244.2666168 2
 
0.4%
927.7701416 2
 
0.4%
261.5091248 2
 
0.4%
508.8981934 2
 
0.4%
401.3128357 1
 
0.2%
342.2649231 1
 
0.2%
414.9378967 1
 
0.2%
361.6979065 1
 
0.2%
Other values (451) 451
96.6%
ValueCountFrequency (%)
193.9132233 1
0.2%
194.2818298 1
0.2%
195.2479248 1
0.2%
201.5090637 1
0.2%
204.9618225 1
0.2%
205.0567932 1
0.2%
206.8027039 1
0.2%
207.0542603 1
0.2%
210.6248474 1
0.2%
229.827774 1
0.2%
ValueCountFrequency (%)
1128.114502 1
0.2%
1126.816528 1
0.2%
1121.274414 1
0.2%
1115.609253 1
0.2%
1109.410767 1
0.2%
1099.34082 1
0.2%
1098.397339 1
0.2%
1080.718384 1
0.2%
1060.175659 1
0.2%
1043.991333 1
0.2%

MAT
Real number (ℝ)

High correlation 

Distinct460
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8795061
Minimum-0.5910638
Maximum16.874287
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)1.3%
Memory size7.3 KiB
2025-09-14T21:16:36.378853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.5910638
5-th percentile1.6072865
Q15.869745
median9.1728353
Q312.444286
95-th percentile14.625114
Maximum16.874287
Range17.46535
Interquartile range (IQR)6.5745409

Descriptive statistics

Standard deviation4.1024038
Coefficient of variation (CV)0.46200811
Kurtosis-0.82904937
Mean8.8795061
Median Absolute Deviation (MAD)3.2931414
Skewness-0.28000736
Sum4146.7293
Variance16.829717
MonotonicityNot monotonic
2025-09-14T21:16:36.485568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.056969643 2
 
0.4%
5.51282835 2
 
0.4%
3.173333406 2
 
0.4%
2.535979271 2
 
0.4%
11.69946766 2
 
0.4%
7.075252533 2
 
0.4%
6.232395649 2
 
0.4%
14.73075008 1
 
0.2%
16.87428665 1
 
0.2%
13.59955025 1
 
0.2%
Other values (450) 450
96.4%
ValueCountFrequency (%)
-0.591063797 1
0.2%
-0.342474163 1
0.2%
-0.309166729 1
0.2%
-0.213022023 1
0.2%
-0.143489569 1
0.2%
-0.103999995 1
0.2%
0.382888913 1
0.2%
0.470781118 1
0.2%
0.702472448 1
0.2%
0.758826554 1
0.2%
ValueCountFrequency (%)
16.87428665 1
0.2%
16.74279022 1
0.2%
16.67824936 1
0.2%
16.67785645 1
0.2%
16.30372429 1
0.2%
16.19720078 1
0.2%
15.98961067 1
0.2%
15.97299957 1
0.2%
15.94797993 1
0.2%
15.7054081 1
0.2%

NDVI
Real number (ℝ)

High correlation 

Distinct461
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43683634
Minimum0.14243349
Maximum0.79699218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:36.592709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.14243349
5-th percentile0.19198515
Q10.30828179
median0.4171764
Q30.55662274
95-th percentile0.72166095
Maximum0.79699218
Range0.65455869
Interquartile range (IQR)0.24834095

Descriptive statistics

Standard deviation0.16185364
Coefficient of variation (CV)0.37051322
Kurtosis-0.91757304
Mean0.43683634
Median Absolute Deviation (MAD)0.13083699
Skewness0.22411246
Sum204.00257
Variance0.0261966
MonotonicityNot monotonic
2025-09-14T21:16:36.701135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.376411051 2
 
0.4%
0.531961083 2
 
0.4%
0.259133458 2
 
0.4%
0.796992183 2
 
0.4%
0.218770355 2
 
0.4%
0.573623061 2
 
0.4%
0.285669565 1
 
0.2%
0.270067871 1
 
0.2%
0.301019162 1
 
0.2%
0.280647784 1
 
0.2%
Other values (451) 451
96.6%
ValueCountFrequency (%)
0.142433494 1
0.2%
0.148967952 1
0.2%
0.153097853 1
0.2%
0.162046626 1
0.2%
0.162069917 1
0.2%
0.163638309 1
0.2%
0.164828882 1
0.2%
0.165527865 1
0.2%
0.167451352 1
0.2%
0.169441774 1
0.2%
ValueCountFrequency (%)
0.796992183 2
0.4%
0.781474531 1
0.2%
0.773537636 1
0.2%
0.770299137 1
0.2%
0.755763054 1
0.2%
0.750859082 1
0.2%
0.750725389 1
0.2%
0.749037266 1
0.2%
0.746697128 1
0.2%
0.737809896 1
0.2%

SiltClay
Real number (ℝ)

High correlation 

Distinct459
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.812998
Minimum9.1619568
Maximum89.834412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-09-14T21:16:36.805351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.1619568
5-th percentile27.003928
Q142.933439
median52.194
Q362.878475
95-th percentile82.939333
Maximum89.834412
Range80.672455
Interquartile range (IQR)19.945036

Descriptive statistics

Standard deviation17.213748
Coefficient of variation (CV)0.31988085
Kurtosis-0.32872154
Mean53.812998
Median Absolute Deviation (MAD)9.9434967
Skewness0.10948128
Sum25130.67
Variance296.3131
MonotonicityNot monotonic
2025-09-14T21:16:36.913980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.89999962 2
 
0.4%
54.03131485 2
 
0.4%
88.55212402 2
 
0.4%
42 2
 
0.4%
33.08124924 2
 
0.4%
53.33737564 2
 
0.4%
31.43030357 2
 
0.4%
58.14123535 2
 
0.4%
67.48787689 1
 
0.2%
41.32395935 1
 
0.2%
Other values (449) 449
96.1%
ValueCountFrequency (%)
9.161956787 1
0.2%
9.253535271 1
0.2%
10.70105267 1
0.2%
12.74591827 1
0.2%
13.47474766 1
0.2%
13.82199955 1
0.2%
14.82857132 1
0.2%
14.99354839 1
0.2%
17.39800072 1
0.2%
17.69052696 1
0.2%
ValueCountFrequency (%)
89.83441162 1
0.2%
88.55212402 2
0.4%
88.5 1
0.2%
88.42424011 1
0.2%
88.42021179 1
0.2%
87.80515289 1
0.2%
87.71546173 1
0.2%
87.42795563 1
0.2%
87.1631546 1
0.2%
87.06046295 1
0.2%

NLCD
Categorical

High correlation 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
Herbaceous
150 
Shrubland
127 
Planted/Cultivated
97 
Forest
93 

Length

Max length18
Median length10
Mean length10.593148
Min length6

Characters and Unicode

Total characters4947
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShrubland
2nd rowShrubland
3rd rowForest
4th rowForest
5th rowForest

Common Values

ValueCountFrequency (%)
Herbaceous 150
32.1%
Shrubland 127
27.2%
Planted/Cultivated 97
20.8%
Forest 93
19.9%

Length

2025-09-14T21:16:37.013686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-14T21:16:37.076630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
herbaceous 150
32.1%
shrubland 127
27.2%
planted/cultivated 97
20.8%
forest 93
19.9%

Most occurring characters

ValueCountFrequency (%)
e 587
11.9%
a 471
 
9.5%
t 384
 
7.8%
u 374
 
7.6%
r 370
 
7.5%
d 321
 
6.5%
l 321
 
6.5%
b 277
 
5.6%
s 243
 
4.9%
o 243
 
4.9%
Other values (11) 1356
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4947
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 587
11.9%
a 471
 
9.5%
t 384
 
7.8%
u 374
 
7.6%
r 370
 
7.5%
d 321
 
6.5%
l 321
 
6.5%
b 277
 
5.6%
s 243
 
4.9%
o 243
 
4.9%
Other values (11) 1356
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4947
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 587
11.9%
a 471
 
9.5%
t 384
 
7.8%
u 374
 
7.6%
r 370
 
7.5%
d 321
 
6.5%
l 321
 
6.5%
b 277
 
5.6%
s 243
 
4.9%
o 243
 
4.9%
Other values (11) 1356
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4947
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 587
11.9%
a 471
 
9.5%
t 384
 
7.8%
u 374
 
7.6%
r 370
 
7.5%
d 321
 
6.5%
l 321
 
6.5%
b 277
 
5.6%
s 243
 
4.9%
o 243
 
4.9%
Other values (11) 1356
27.4%

FRG
Categorical

High correlation 

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
Fire Regime Group II
250 
Fire Regime Group III
100 
Fire Regime Group IV
73 
Fire Regime Group I
 
19
Fire Regime Group V
 
18

Length

Max length21
Median length20
Mean length20.089936
Min length17

Characters and Unicode

Total characters9382
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFire Regime Group IV
2nd rowFire Regime Group IV
3rd rowFire Regime Group V
4th rowFire Regime Group V
5th rowFire Regime Group V

Common Values

ValueCountFrequency (%)
Fire Regime Group II 250
53.5%
Fire Regime Group III 100
 
21.4%
Fire Regime Group IV 73
 
15.6%
Fire Regime Group I 19
 
4.1%
Fire Regime Group V 18
 
3.9%
Indeterminate FRG 7
 
1.5%

Length

2025-09-14T21:16:37.163412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-14T21:16:37.234958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fire 460
24.8%
regime 460
24.8%
group 460
24.8%
ii 250
13.5%
iii 100
 
5.4%
iv 73
 
3.9%
i 19
 
1.0%
v 18
 
1.0%
indeterminate 7
 
0.4%
frg 7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 1401
14.9%
1387
14.8%
r 927
9.9%
i 927
9.9%
I 899
9.6%
F 467
 
5.0%
m 467
 
5.0%
G 467
 
5.0%
R 467
 
5.0%
g 460
 
4.9%
Other values (8) 1513
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1401
14.9%
1387
14.8%
r 927
9.9%
i 927
9.9%
I 899
9.6%
F 467
 
5.0%
m 467
 
5.0%
G 467
 
5.0%
R 467
 
5.0%
g 460
 
4.9%
Other values (8) 1513
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1401
14.9%
1387
14.8%
r 927
9.9%
i 927
9.9%
I 899
9.6%
F 467
 
5.0%
m 467
 
5.0%
G 467
 
5.0%
R 467
 
5.0%
g 460
 
4.9%
Other values (8) 1513
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1401
14.9%
1387
14.8%
r 927
9.9%
i 927
9.9%
I 899
9.6%
F 467
 
5.0%
m 467
 
5.0%
G 467
 
5.0%
R 467
 
5.0%
g 460
 
4.9%
Other values (8) 1513
16.1%

Interactions

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2025-09-14T21:16:21.681102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:22.790212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:23.906732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:24.971409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:26.538763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:27.679969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:28.747358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:29.887016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:31.010401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:32.223575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:17.477413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:18.485451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:19.596375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:20.669268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:21.760187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:22.873573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:23.985513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:25.047860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:26.610928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:27.764232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:28.821676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:29.973036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:16:31.084189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-14T21:16:37.332049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AspectDEMFIPSFRGIDKFactorLatitudeLongitudeMAPMATNDVINLCDSOCSTATESTATE_IDSiltClaySlopeTPI
Aspect1.0000.2290.0210.228-0.252-0.084-0.006-0.2520.112-0.1950.1250.2500.0860.1360.136-0.0610.3190.060
DEM0.2291.0000.0190.410-0.793-0.5700.103-0.793-0.205-0.811-0.0290.5770.0900.5740.574-0.4460.734-0.000
FIPS0.0210.0191.0000.420-0.3530.1490.293-0.353-0.311-0.129-0.3400.457-0.1091.0001.000-0.1510.0910.006
FRG0.2280.4100.4201.0000.4300.2430.3000.4090.2600.4130.2390.5180.1230.4200.4200.2070.3600.249
ID-0.252-0.793-0.3530.4301.0000.386-0.1651.0000.4310.6380.2970.5340.0790.6000.6000.388-0.6620.015
KFactor-0.084-0.5700.1490.2430.3861.0000.1200.3860.0630.344-0.0410.3010.0440.3580.3580.596-0.402-0.030
Latitude-0.0060.1030.2930.300-0.1650.1201.000-0.165-0.007-0.6060.2020.3520.2820.8130.813-0.0320.187-0.025
Longitude-0.252-0.793-0.3530.4091.0000.386-0.1651.0000.4310.6380.2970.5370.0790.6200.6200.388-0.6620.015
MAP0.112-0.205-0.3110.2600.4310.063-0.0070.4311.0000.0670.8520.4050.5740.4110.4110.4350.0650.165
MAT-0.195-0.811-0.1290.4130.6380.344-0.6060.6380.0671.000-0.1970.469-0.2930.5950.5950.291-0.6520.002
NDVI0.125-0.029-0.3400.2390.297-0.0410.2020.2970.852-0.1971.0000.4630.6480.3840.3840.2990.1830.124
NLCD0.2500.5770.4570.5180.5340.3010.3520.5370.4050.4690.4631.0000.2640.4570.4570.3760.4920.317
SOC0.0860.090-0.1090.1230.0790.0440.2820.0790.574-0.2930.6480.2641.0000.2300.2300.2890.2910.059
STATE0.1360.5741.0000.4200.6000.3580.8130.6200.4110.5950.3840.4570.2301.0001.0000.4820.3120.178
STATE_ID0.1360.5741.0000.4200.6000.3580.8130.6200.4110.5950.3840.4570.2301.0001.0000.4820.3120.178
SiltClay-0.061-0.446-0.1510.2070.3880.596-0.0320.3880.4350.2910.2990.3760.2890.4820.4821.000-0.177-0.032
Slope0.3190.7340.0910.360-0.662-0.4020.187-0.6620.065-0.6520.1830.4920.2910.3120.312-0.1771.0000.039
TPI0.060-0.0000.0060.2490.015-0.030-0.0250.0150.1650.0020.1240.3170.0590.1780.178-0.0320.0391.000

Missing values

2025-09-14T21:16:32.368572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-14T21:16:32.500476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDFIPSSTATE_IDSTATECOUNTYLongitudeLatitudeSOCDEMAspectSlopeTPIKFactorMAPMATNDVISiltClayNLCDFRG
015604156WyomingUinta County-111.01186041.05630015.7632229.078613159.1877445.671615-0.0857240.320000468.3244934.5951690.41393964.842697ShrublandFire Regime Group IV
125602356WyomingLincoln County-110.98297342.88349715.8831889.400146156.8785558.9138124.5591320.261212536.3521733.8599240.69395372.004547ShrublandFire Regime Group IV
235603956WyomingTeton County-110.80649044.53497018.1422423.048340168.6123504.7748052.6058870.216200859.5509030.8855000.54660357.187000ForestFire Regime Group V
345603956WyomingTeton County-110.73441744.43288610.7452484.282715198.3536227.1218115.1469310.181667869.4724120.4707810.61910154.991665ForestFire Regime Group V
455602956WyomingPark County-110.73079044.80635010.4792396.194580201.3214877.9498643.7557060.125510802.9743040.7588270.58447251.228573ForestFire Regime Group V
565603956WyomingTeton County-110.66185044.09124016.9872360.572998208.9731609.6632156.5021560.2682221121.2744141.3586670.60283545.020000ForestFire Regime Group V
675603956WyomingTeton County-110.64348043.51083024.9542254.791260254.6745768.940430-9.3831870.184316610.8505862.8187890.52256150.295788ForestFire Regime Group IV
785603956WyomingTeton County-110.59581944.3181506.2882496.393555189.52731312.5829506.3335410.2038891109.4107670.3828890.46377352.413334ShrublandFire Regime Group V
895603956WyomingTeton County-110.57698043.69342021.4552238.686768234.4831249.6814111.0905850.319785817.9403082.1004840.65254149.159142ForestFire Regime Group IV
9105603556WyomingSublette County-110.51702043.17939021.6442228.609863152.17927515.030870-6.7860530.058526561.0355221.9427370.69170344.122105ForestFire Regime Group IV
IDFIPSSTATE_IDSTATECOUNTYLongitudeLatitudeSOCDEMAspectSlopeTPIKFactorMAPMATNDVISiltClayNLCDFRG
4614642000520KansasAtchison County-95.52098739.56081610.728323.997589180.0093081.8581060.3301250.311263937.00988812.2180530.72608374.167366Planted/CultivatedFire Regime Group II
4624652008720KansasJefferson County-95.48475339.13829712.903296.393372184.4024053.3716653.3688280.368571954.42529312.4502380.75085979.306351Planted/CultivatedFire Regime Group II
4634662009920KansasLabette County-95.47712437.2619827.256272.679688185.0403901.4299681.0744590.3255291099.34082013.7481170.69431477.164703Planted/CultivatedFire Regime Group I
4644672001320KansasBrown County-95.37215339.8823297.500313.830322176.8919982.361841-0.8858890.280000927.77014211.6994680.79699288.552124Planted/CultivatedFire Regime Group II
4654682009920KansasLabette County-95.36933137.12324013.147269.335358177.4589231.011653-0.1606410.3859571115.60925313.9399470.67338681.658508Planted/CultivatedFire Regime Group II
4664692001320KansasBrown County-95.36515639.8479886.433313.830322176.8919982.361841-0.8858890.280000927.77014211.6994680.79699288.552124Planted/CultivatedFire Regime Group II
4674702013320KansasNeosho County-95.23890937.6914218.780293.830933184.4200741.3907490.6099010.3587911098.39733913.6428020.72152683.907692Planted/CultivatedFire Regime Group II
4684712000120KansasAllen County-95.18900037.93123010.672323.660980147.1656801.0671600.5195720.3422111080.71838413.4258950.69066674.418945Planted/CultivatedFire Regime Group II
4694722004320KansasDoniphan County-95.01079439.8113064.488298.053680173.3465123.1455341.1193820.287957935.52948011.8026340.77353889.834412Planted/CultivatedFire Regime Group II
4704732002120KansasCherokee County-94.91542137.2844936.975272.262390194.9636840.917755-0.2569610.1926031126.81652813.8499320.66175879.964386Planted/CultivatedFire Regime Group V